title: Feature selection and survival modeling in The Cancer Genome Atlas.
abstract: OBJECTIVE
  Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling.
  
  
  METHODS
  The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1) 1-nearest neighbor (1-NN) survival prediction method; (2) random patient selection method and a Cox-based regression method with nested cross-validation; (3) least absolute shrinkage and selection operator (LASSO) optimization using whole-genome gene expression profiles; or (4) gene expression profiles of cancer pathway genes.
  
  
  RESULTS
  The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone.
  
  
  CONCLUSIONS
  The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology.
authors.affiliation: Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: Hyunsoo
authors.initials: H
authors.lastname: Kim
authors.forename: Markus
authors.initials: M
authors.lastname: Bredel
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005909:Glioblastoma; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D012044:Regression Analysis; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor
keywords: TCGA; brain; feature selection; glioblastoma; personalized medicine; survival modeling